Evaluation of key figures for the prediction of project-related costs: A study of production equipment in the automotive supply industry.

Goeer, Peter.
(2011)
Evaluation of key figures for the prediction of project-related costs: A study of production equipment in the automotive supply industry.
Doctoral thesis, University of Surrey (United Kingdom)..

Abstract

The research question is to examine and predict project-related costs during the preplanning and post-cost-calculating phase. The aim of this thesis is to identify costs earlier, which means before or at the beginning of the product planning phase, and faster, although the documents are not yet complete and the product is not defined in detail. The objective of the thesis is oriented on short cost calculation systems where a few cost-relevant key figures can be used to predict manufacturing costs at the conceptual stage of the product when the costs can be still significantly affected. The research is conducted on the company ThyssenKrupp Drauz Nothelfer, Germany, a major supplier of production equipment for the automotive industry. The epistemology of the research is based on the process of data collection and resulted in an overall conceptual framework of the research exploration. The methodology employed a deductive top-down logical system that includes descending from the more general information of the theory to the hypothesis. Seventy-three projects have been analysed and evaluated regarding the thesis approach. The method involves the analysis of projects and existing documentation. The qualitative method was used in order to verify the research results. Several cost-relevant key figures were found from data already available at the conceptual design stage. The independent variables were also checked for correlation, analysed and data limited. From these parameters, two variables were emphasised, and with the help of the parametric method with regression analysis, an equation was formed. The results show that a significant linear relationship exists between these parameters and the manufacturing costs of the product. The stated regression equation can be used throughout the early planning phase of a project to set a budget for the ongoing planning process, or to optimise the costs of the product by running the model parallel to the planning process.